PulseBound is a photoplethysmography (PPG) representation learner that enforces an explicit stored‑window information boundary, ensuring that encoder inputs depend only on a visible prefix and a content‑independent cutoff. By combining physiologically structured future‑beat prediction with prefix‑only normalization, suffix replacement, and aligned masking, PulseBound achieves stored‑suffix invariance, preventing changes in the stored suffix from affecting forecast context or predictions. Evaluated on MIMIC and VitalDB datasets, PulseBound reduces mean absolute error by over 22% compared to last‑visible‑beat persistence and outperforms seven other models on nine frozen linear‑probe and seven full‑fine‑tuning downstream tasks.
arXiv:2608. 12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences.
arXiv:2606. 15284v1 Announce Type: cross Abstract: Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support.
arXiv:2607. 22264v1 Announce Type: new Abstract: Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on structured event codes alone, and do not incorporate multiple modalities in a principled way.
arXiv:2609.36794v1 Announce Type: new Abstract: Signal features derived from photoplethysmography (PPG) require different signal durations to characterize. Existing PPG foundation models treat durati...
PPG-Bench is an open‑source benchmark that evaluates five publicly available photoplethysmography (PPG) foundation models across nine datasets and 26 tasks, covering cardiovascular state estimation, activity, sleep, screening, and identification. The benchmark tests models under four downstream regimes—linear probing, random forests, frozen MLP probes, and end‑to‑end finetuning—while also comparing them to three controls: a random/constant baseline, a deterministic pulse‑rate‑variability feature set, and a generic time‑series foundation model (MOMENT). Results show that over a third of task‑regime combinations yield no advantage for any PPG foundation model over the baseline, and in most cases no PPG‑specific model outperforms the simple controls, indicating that current PPG foundation models lack generalised, transferable representations.
PPG-to-vital-sign reconstruction turns a wrist-worn photoplethysmogram into clinical waveforms such as the ECG. Long-horizon multivariate time-series forecasting underpins planning in energy, weather,...
arXiv:2608. 00943v1 Announce Type: cross Abstract: Automated sleep staging assigns discrete stage labels to successive time epochs throughout an overnight recording; conventionally each window spans at least 30 seconds, reflecting the minimum temporal resolution of the clinical scoring standard.
The paper introduces ROOSTER, a shared relative‑alignment module that learns how to map a condition sequence to a target sequence for both PPG‑to‑vital‑sign reconstruction and long‑horizon multivariate time‑series forecasting. ROOSTER uses a learnable periodic‑comb bias over the target‑condition offset, allowing it to discover identity alignment or seasonal lags and report the chosen correspondence. Experiments show that ROOSTER outperforms existing baselines on heart‑rate and respiratory‑rate reconstruction from wrist‑worn photoplethysmograms, and achieves the lowest horizon‑averaged MSE on four forecasting benchmarks, outperforming the underlying forecasting model in most dataset‑horizon settings.
arXiv:2605. 10840v3 Announce Type: replace-cross Abstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories.
Neural State Prediction (NSP) is a latent‑predictive framework designed to curb shortcut learning in EEG foundation models. By using a target encoder updated with an exponential moving average, identity residualization, and topology‑separated context, NSP constrains both the prediction target and the available context. Trained on 2.2 million EEG segments, NSP outperforms baselines on 14 datasets in the EEG‑FM‑Bench, achieving 63.94 % macro balanced accuracy.
arXiv:2607. 23554v1 Announce Type: cross Abstract: In this paper, we propose the MAEConformer, a novel self-supervised learning framework that combines the Conformer architecture with the Masked Autoencoder (MAE) paradigm for large-scale representation learning from unlabelled electroencephalography (EEG) and heart rate variability (HRV) signals.
The paper presents a hybrid CNN–state‑space–attention backbone designed for 12‑lead ECG classification, combining early waveform tokenization, mixed temporal dynamics modeling, and late global attention. It introduces an ECG‑oriented Joint‑Embedding Predictive Pretraining (JEPA) that samples span masks at latent resolution and predicts clean latent targets via a momentum encoder, avoiding waveform reconstruction. Experiments on CPSC2018, Chapman‑Shaoxing, and PTB‑XL, with pretraining on ~350K unlabeled CODE‑15 recordings, demonstrate strong supervised baselines and improved transfer, especially in low‑label scenarios and with LoRA adaptation.